HGDC 2026荣耀天工行业解决方案打造企业级AI全栈新体系

General readers may focus on core details of Honor’s Tiangong industry solution, as well as practical takeaways relevant to daily work and AI tool selection.

1. Unveiled at HGDC 2026, the solution is Honor’s enterprise-grade, full-stack AI system designed to address common pain points in government and enterprise AI deployment: generic models lacking industry-specific context, high integration costs, unpredictable token expenses, and security concerns over uploading core data to the cloud. It covers full-scenario deployments from personal productivity devices to organizational AI workstations.

2. For personal AI office device selection, users can directly reference the tiered product positioning: entry-level employees and users with basic office needs can opt for the L3 AI PC to handle daily work and meeting requirements; power users managing multitasking workloads can choose the upgradable L5 model; the L7 is tailored for heavy-computing scenarios for senior executives and engineering professionals; frequent business travelers can select the ultra-thin, highly portable L9 model.

3. The solution resolves many common daily office pain points, such as automating report processing, enabling fast internal document retrieval, and integrating with widely used tools like WPS to boost productivity. Its local deployment capability also prevents leaks of sensitive work materials, balancing efficiency and data security.

Brand owners may focus on the user insight, product R&D, and ecosystem marketing experience behind Honor’s Tiangong industry solution, to inform their own brand building and business expansion efforts.

1. In user demand insight, Honor precisely identifies four core pain points for government and enterprise AI adoption, while segmenting demand across different workforce groups: basic office users prioritize cost-effectiveness, technical staff prioritize computing power, and frequent travelers prioritize portability—an approach that avoids vague product positioning.

2. In product R&D, Honor adopts a tiered layout logic, offering four performance grades for both AI PCs and AI workstations to match diverse needs ranging from entry-level office work to local large language model (LLM) deployment, solving customers’ selection dilemma of “buying too little to meet needs, or buying too much and wasting resources.” Its three self-developed supporting platforms deliver the core values of AI-powered efficiency gains, cost reduction, and security compliance.

3. In brand ecosystem marketing, Honor collaborates with cross-sector leading partners including Intel, AMD, Alibaba, Kingsoft WPS, and Tongdaxin to build a cross-domain ecosystem covering chips, development, office work, finance and other scenarios, strengthening market recognition of the solution’s practical implementability and reducing user trust barriers.

Relevant sellers may focus on the growth opportunities, replicable business models, and partnership pathways unlocked by Honor’s Tiangong solution, to capture market dividends from AI adoption.

1. Core growth markets are clearly defined: Government and enterprise demand for intelligent transformation is currently urgent, but deployment is slowed by poor model fit, high costs, and data security risks. The solution covers four high-value sectors—finance, manufacturing, legal services, and public administration—while its tiered AI devices meet both individual procurement (from entry-level employees to senior business professionals) and centralized enterprise procurement needs, creating broad market space.

2. The solution provides a flexible, referenceable business model with three service tiers to match different customer needs: customers who only need device replacement can choose the lightweight, pure-hardware deployment with no subscription fees; customers seeking general office AI capabilities can select the efficiency package combining hardware with on-device general-purpose models; vertical industry customers with customization needs can opt for the full-stack, integrated deep transformation service, significantly lowering customer conversion barriers.

3. Clear partnership pathways are available: Upholding an open, win-win philosophy, Honor fully opens up its technology and platform resources. Both hardware/software vendors and channel partners can integrate their capabilities into the intelligent agent ecosystem, co-develop implementation cases, and share industry dividends.

Manufacturing factories may focus on the product design directions, internal digital transformation pathways, and potential business cooperation opportunities brought by the solution.

1. Product design and production can reference the tiered adaptation logic: For intelligent hardware in the AI era, manufacturers can build graded product lines aligned with usage scenarios and performance requirements. Following the tiered approach of Honor’s AI PCs and workstations, products can match diverse needs from basic office work to heavy computing and mobile portability, precisely addressing users’ selection priority of “no wasted computing power, sufficient performance for needs.”

2. Mature solutions are available as reference for internal digital transformation: To address manufacturing pain points such as low efficiency, missed defects and false detections in manual quality inspection, factories can leverage the solution’s AI inspection and fault troubleshooting knowledge base capabilities to replace traditional manual processes, improve production yield, and build a real-time, closed-loop, traceable on-device production management system.

3. Abundant potential business opportunities exist: Honor is collaborating with full value chain partners to build its ecosystem. Factories can participate in hardware supply chain supporting and industry scenario adaptation, leveraging Honor’s platform resources and channel network to access large volumes of orders from government and enterprise intelligent transformation demand.

Enterprise service providers may focus on AI industry trends, common customer pain points, and implementable solution frameworks to optimize their own service capabilities.

1. Industry development trends are clear: Enterprise AI services have moved past the fragmented, single-point tool assistance phase into the full-stack service era defined by “hardware foundation + capability platform + ecosystem scenarios.” Intelligent agents have become the core vehicle for connecting disparate systems and delivering scenario value, while on-device AI deployment is seeing rapid demand growth due to its security advantages.

2. Core customer pain points are well defined: Government and enterprise customers currently face four universal bottlenecks in AI deployment: generic models lack vertical business context, system integration is complex and capital-intensive, token costs are unpredictable, and uploading core data to the cloud carries security risks. Vertical sectors also face scenario-specific pain points including slow security response, easy document leakage, labor-intensive report processing, and low quality inspection efficiency.

3. The solution offers strong reference value: Providers can follow a combined pathway of flexible underlying computing power configuration, platform-level algorithm optimization to reduce usage costs, agent-based integration with customers’ existing systems without reconstruction, and operation and maintenance design that meets compliance requirements. Combined with vertical scenario customization, this approach delivers tangible AI value to frontline business operations.

Platform enterprises may focus on enterprise customers’ core demands for AI platforms, platform capability building methods, and practical experience in ecosystem operation and partner recruitment.

1. Core enterprise demands for AI platforms are explicit: Enterprises do not want to overhaul existing business systems to deploy AI. Instead, they need platforms that can quickly integrate with current OA, ERP and other systems, reduce AI usage costs in a controllable manner, ensure core data security and compliance, and enable operability and manageability across the full lifecycle of AI applications.

2. A mature pathway for platform capability building is available for reference: Platforms need to build three core capabilities: First, inference optimization, which improves inference speed and reduces token costs through algorithm and chip-level adaptation; second, intelligent agent scheduling, which enables cross-application process automation and improves customization efficiency for vertical domain scenarios; third, security operation and maintenance, which delivers full-process visibility, disaster recovery backup, and compliance certification to address customer security concerns.

3. Clear directions for ecosystem operation and recruitment: Platforms can open APIs to attract partners across sectors including chips, office software, industry services, and development tools to co-build vertical scenario skills. At the same time, tiered service packages can be designed to match the needs of customers of different scales, lowering access barriers for customers.

Industry researchers may focus on the new AI industry issues, trends, and innovative business models reflected in the solution, which provide a real-world sample for industry research.

1. Common core industry pain points are clear: Government and enterprise AI deployment has moved past the technology concept popularization stage into the substantive implementation phase, but generally faces four bottlenecks: insufficient fit between general-purpose LLMs and industry-specific business workflows, high upfront investment due to complex system integration, hard-to-predict token consumption costs, and security risks of uploading core data to the cloud, keeping overall transformation barriers high.

2. New industry development trends are evident: Consumer electronics vendors, especially PC manufacturers, are expanding from personal AI terminal layouts to organizational AI productivity scenarios, breaking down transformation barriers via full-stack systems combining “underlying hardware computing power + middle-layer self-developed platforms + upper-layer open ecosystems.” Intelligent agents are becoming the core interaction vehicle, while on-device LLM deployment and local data closed loops are emerging as the mainstream approach to meet security and compliance requirements.

3. The business model holds innovative reference value: The solution’s model of tiered hardware adaptation, three flexible deployment tiers, and cross-domain ecosystem co-creation provides a replicable transformation template for multiple industries. Open, win-win ecosystem collaboration is becoming the core pathway for scaled AI implementation across industries.